Statistical Methods for Healthcare Performance Monitoring: 1st Edition (Hardback) book cover

Statistical Methods for Healthcare Performance Monitoring

1st Edition

By Alex Bottle, Paul Aylin

CRC Press

269 pages | 12 B/W Illus.

Purchasing Options:$ = USD
Hardback: 9781482246094
pub: 2016-08-01
eBook (VitalSource) : 9781315372778
pub: 2016-08-05
from $48.48

FREE Standard Shipping!


Healthcare is important to everyone, yet large variations in its quality have been well documented both between and within many countries. With demand and expenditure rising, it’s more crucial than ever to know how well the healthcare system and all its components – from staff member to regional network – are performing. This requires data, which inevitably differ in form and quality. It also requires statistical methods, the output of which needs to be presented so that it can be understood by whoever needs it to make decisions.

Statistical Methods for Healthcare Performance Monitoring covers measuring quality, types of data, risk adjustment, defining good and bad performance, statistical monitoring, presenting the results to different audiences and evaluating the monitoring system itself. Using examples from around the world, it brings all the issues and perspectives together in a largely non-technical way for clinicians, managers and methodologists.

Statistical Methods for Healthcare Performance Monitoring is aimed at statisticians and researchers who need to know how to measure and compare performance, health service regulators, health service managers with responsibilities for monitoring performance, and quality improvement scientists, including those involved in clinical audits.


"… Overall, the book provides an interesting and easily accessible overview on health care performance monitoring and the statistical methods associated with it. Each chapter has a short overview at the beginning and sometimes a conclusion at the end, so it can also serve as a reference book. According to the authors, this book is not primarily aimed at statisticians, but all who want to compare and measure health care performance. The level of statistics obtained from an undergraduate nursing or medical degree is enough to follow. Furthermore, the authors marked several chapters and subchapters more heavy on statistics that can be skipped without missing the big picture. The rich examples make the book enjoyable to read and it has my unconditional recommendation to all interested in the topic."

—Christoph F. Kurz, Helmholtz Zentrum Munich, in Biometrics, March 2018

"Bottle and Aylin offer readers a practical approach to performance measurement, and the statistical tools to get it right. This topic may seem dry and arcane until you realize that these methods are what patients and policymakers depend on to tell a good hospital from a dangerous one, and a superb physician from a quack. In fact, the authors learned their trade investigating some of the most famous cases of medical scandals in the world, including a hospital whose errors killed dozens of babies, and a murderous physician who killed scores of patients. So, the lessons in this book matter. Highly recommended."

Robert M. Wachter, MD, Professor and Chair, Department of Medicine, University of California, San Francisco

"Improving healthcare and ensuring patient safety relies on timely, valid and understandable information. Healthcare systems are awash with data but this seldom translates into useful information. The great value of this book lies in the combination of statistical sophistication with an understanding of the healthcare context and a practical concern for improving the care of patients. Alex Bottle and Paul Aylin have done us a great service by sharing their extensive expertise and showing us how healthcare can be effectively monitored and improved."

—Charles Vincent, Professor of Psychology, University of Oxford, and Emeritus Professor Clinical Safety Research, Imperial College London

"This book is the most thorough, comprehensive and practical review of hospital performance monitoring available to my knowledge. ? Although a statistical manual, it is not overly technical with very few formulae, and covers the ground in a logical way. It is replete with examples and I particularly like the tabulations of pros and cons of different methods and approaches and summaries of current controversies.

It discusses real-world issues which affect policy makers, practitioners and researchers alike and will be of value to all. I wish I'd had this book when I was working in this area. I can strongly recommend to anyone wishing to embark on the complexities of performance monitoring, and everyone who is already engaged in this area."

Julian Flowers, Head of Public Health Data Science, Public Health England

Table of Contents


The need for performance monitoring

Measuring and monitoring quality

The need for this book

Who is this book for and how should it be used?

Common abbreviations used in the book


Origins and examples of monitoring systems


Healthcare scandals

Examples of monitoring schemes

Goals of monitoring

Choosing the unit of analysis and reporting

Issues principally concerning the analysis

Issues more relevant to reporting: attributing performance to a given unit in a system

What to measure: choosing and defining indicators

How can we define quality?

Common indicator taxonomies

The particular challenges of measuring patient safety

The particular challenges of multimorbidity

Measuring the health of the population and quality of the whole healthcare system

Efficiency and value

Features of an ideal indicator

Steps in construction and common issues in definition

Validation of indicators

Some strategies for choosing among candidates

Time to go: when to withdraw indicators


Sources of data

How to assess data quality

Administrative data

Clinical registry data

The accuracy of administrative and clinical databases compared

Indicent reports and other ways to capture safety events


Other sources

Other issues concering data sources


Risk-adjustment principles and methods

Risk adjustment and risk prediction

When and why should we adjust for risk?

Alteratives to risk adjustment

What factors should be adjust for?

Selecting an initial set of candidate variables

Dealing with missing and extreme values

Timing of the risk factor measurement

Building the model

Output the observed and model-predicted outcomes

Ratios versus differences

Deriving SMRs from standardisation and logistic regression

Other fixed effects approaches to generate an SMR

Random effects based SMRs

Marginal versus multilevel models

Which is the "best" modelling approach overall?

Further reading on producing risk-adjusted outcomes by unit

Composite measures

Some examples

Steps in the construction

Some real examples

Pros and cons of composites

Setting performance thresholds and defining outliers

Defining acceptable performance

Bayesian methods for comparing providers

Statistical process control and funnel plots

Multiple testing

Ways of assessing variation between units

How much variation is "acceptable"?

The impact on outlier status of using fixed versus random effects to derive SMRs

How reliably can we detect poor performance?

Some resources for quality improvement methods

Making comparisons across national borders

Examples of multinational patient-level databases


Interpreting apparent differences in performance between countries


Presenting the results to stakeholders

Main ways of presenting comparative performance data

Effect on behaviour of the choice of format when providing performance data

Importance of the method of presentation

Examples of giving performance information to units

Examples of giving performance information to the public


Evaluating the monitoring system

Study design and statistical approaches to evalutating a monitoring system

Economic evaluation methods

Concluding thoughts

Simple versus complex

Specific versus general

The future


Appendix: glossary of main statistical terms used

About the Authors

Dr. Alex Bottle is a Senior Lecturer at Imperial College London, UK

Dr. Paul Aylin is a Professor at Imperial College London, UK

About the Series

Chapman & Hall/CRC Biostatistics Series

Learn more…

Subject Categories

BISAC Subject Codes/Headings:
MATHEMATICS / Probability & Statistics / General
MEDICAL / Administration
MEDICAL / Biostatistics